No description available.

Localization is a critical aspect of robots and their industrial applications, with its major impact on navigation and planning. The goal of this thesis is to improve multi-robot localization by utilizing scan matching algorithms to calculate a corrected pose estimate using the robots' shared laser scan data. The current pose estimate relative to the map is used as the initial guess for the scan matching. This corrected pose is fused using several different localization configurations, such as an Extended Kalman Filter in combination with the Adaptive Monte Carlo Localization algorithm. Simulations showed that localization improved by resetting the Monte Carlo particle filter with the pose estimate generated by the collaborative scan matching. Further, in simulated scenarios, the collaborative scan matching implementation improved the accuracy of typical Monte Carlo Localization configurations. Furthermore, when filtering based on the number of reciprocal correspondences between the scan match output and the target scan, one could extract highly accurate pose estimates. When resetting the Monte Carlo Localization algorithm with the pose estimates, the localization algorithm could successfully recover from severe errors in the global positioning. In future work, additional testing needs to be done using these extracted pose estimates in a dedicated map-based multi-robot localization algorithm.

Driven by the advancement of smart electrical grid technologies, automated home energy management systems are being increasingly and extensively studied, developed, and widely accepted. A system like this is indispensable for and symbolic of a smart home. Mixed integer linear programming (MILP) together with dynamic electricity tariff and smart home appliances is a common way to developing energy management systems capable of automatically scheduling appliance operation and greatly saving monetary cost. This study transformed an existing plain MILP model to a goal programming model with priority to better address the conflict among each single appliance cost saving objective and user time preference objective. Constraints regarding the delays between pairs of closely related appliances are either extended or newly introduced. Numerical experiments on five appliances under different situations justify the validness of the proposed framework. Besides, the influences of key parameters on model performance are also investigated.

Robotic Process Automation (RPA) is an automation technology that has become increasingly common in recent years, as demands have increased on the use of new technologies to compete in the global economy. RPA means that a software robot is placed on top of existing systems where it performs tasks in a similar way as a human being previously did. No changes need to be made to the underlying systems, which means that RPA is rather quick and easy to implement at a relatively low cost. There are many advantages to the usage of RPA, but not all processes are suitable for the implementation of robots. The purpose of the study has been to help businesses select the parts of the organization that can be automated by identifying criteria which means that RPA can be applied to a process. In order to fulfill the purpose of the study, information has been collected through a literature study and semi-structured interviews with consultants working in the RPA area. The result shows that there are several criteria that increase the suitability of an RPA implementation on a process, but also that the importance of different criteria varies from situation to situation. The study has also identified criteria that must be met in order to be able to implement robots at all. Since the appearance of different companies and processes varies greatly, the criteria should be seen as a guideline where each situation is examined individually to identify what is important right there. Initially, a detailed analysis needs to be done of a candidate process in order to determine the degree of suitability and whether RPA is a good solution in that case. / Robotic Process Automation (RPA) är en automatiseringsteknik som blivit allt vanligare de senaste åren. Kraven har ökat på användandet av nya teknologier för att kunna konkurrera i den globala ekonomin. RPA innebär att en mjukvarurobot placeras ovanpå befintliga system där den utför uppgifter på ett likadant sätt som en människa tidigare gjorde. Inga ändringar behöver göras på de underliggande systemen vilket medför att RPA är förhållandevis snabbt och enkelt att införa med relativt låg kostnad. Det finns många fördelar med användningen av RPA, men alla processer är inte lämpliga att implementera robotar på. Studiens syfte har varit att hjälpa verksamheter att välja ut de delar i organisationen som kan automatiseras genom att identifiera kriterier som medför att RPA går att använda på en process. För att uppfylla studiens syfte har information samlats in via en litteraturöversikt och semistrukturerade intervjuer med konsulter som arbetar inom RPA-området. Resultatet visar att det finns ett flertal kriterier som ökar lämplighetsgraden för införandet av RPA på en process, men också att vikten av olika kriterier varierar från situation till situation. Studien har även identifierat kriterier som måste uppfyllas för att det överhuvudtaget ska gå att implementera robotar. Eftersom utseendet på olika företag och processer varierar kraftigt så bör kriterierna ses som en riktlinje där varje situation utreds individuellt för att identifiera vad som är viktigt just där. Initialt behöver en utförlig analys göras av en kandidatprocess för att kunna fastställa lämplighetsgraden och om RPA är en bra lösning i det fallet.

Convolutional neural networks have been successfully used in action recognition but are usually restricted to operate on Euclidean data, such as images. In recent years there has been an increase in research devoted towards finding a generalized model operating on non-Euclidean data (e.g graphs) and manipulation action recognition on graphs is still a very novel subject. In this thesis a novel graph based deep neural network is developed for predicting manipulation actions and reconstructing graphs from a lower space representation. The network is trained on two manipulation action datasets and uses their, respective, previous works on action prediction as a baseline. In addition, a modular perception pipeline is developed that takes RGBD images as input and outputs a scene graph, consisting of objects and their spatial relations, which can then be fed to the network to lead to online action prediction. The network manages to outperform both baselines when training for action prediction and achieves comparable results when trained in an end-to-end manner performing both action prediction and graph reconstruction, simultaneously. Furthermore, to test the scalability of our model, the network is tested with input graphs deriving from our scene graph generator where the subject is performing 7 different demonstrations of the learned action types in a new scene context with novel objects.

I robotikkursen Autonoma Mekatroniska System på Halmstad Högskola finns ett problem som kan uppstå vid bygget av deras hjulförsedda mobila robotar. Studenter har en tendens att bygga roboten så att den körs med maximal effekt från start till inbromsning. Detta medför att vid test av deras robotar så slits motorerna ut fortare vilket ger en onödig kostnad. För att minska på kostnaden är målet med detta projekt att bygga och reglera en sådan robot med hjälp av en banplanerare. Banplaneraren skapar mjuka övergångar vid start och inbromsning av roboten vilket gör att motorerna inte slits ut lika fort. I detta projekt ska vi utveckla en robot som kan användas som ett pedagogiskt undervisningsmaterial. Roboten ska vara en mall i kursen för studenterna så de kan se att det går bygga en robot som kan köras med mjuka övergångar. Roboten ska både kunna följa ett rörligt objekt med ett bestämt avstånd och har möjlighet att rotera. I detta projekt har även en undersökning av en modellerad och en verklig motor gjorts genom systemidentifiering. Resultatet blev en robot som kan följa ett rörligt objekt med ett bestämt avstånd där start och inbromsning görs mjukt. Med denna robot kan studenter se att det är möjligt att bygga en robot som körs med mjuka övergångar, vilket leder till mindre slitage av motorerna. Stegsvar och bodediagram har plottats fram genom systemidentifieringen där skillnader och likheter mellan verklig och modellerad motor kan ses. / In the robotics course Autonomous Mechatronic Systems at Halmstad University there is a problem that may occur during the construction of their wheeled mobile robots. Students have a tendency to build the robot so that it runs with maximum efficiency from start to braking. This means that when testing their robots, the motors wear out faster, which is providing an unnecessary cost. To reduce the cost, the goal of this project is to build and regulate such a robot using a positionplanner. The positionplanner creates smooth transitions at start and braking of the robot so that the motors does not wear out as fast. In this project, we will develop a robot that can be used as an educational teaching materials. The robot will be a template in the course for the students so they can see that it is possible to build a robot that can run with smooth transitions. The robot should both be able to follow a moving object with a fixed distance and be able to rotate. In this project, an investigation of a modeled and actual motor has been made through a systemidentification. The result was a robot that can follow a moving object with a fixed distance where starting and braking is done smoothly. With this robot, students can see that it is possible to build a robot running with smooth transitions, which leads to less wear on the motors. Stepresponses and bodeplots have been plotted through the systemidentification where differences and similarities between real and modeled motor can be seen.

Den obemannade tröskeln är en uppsats på magisternivå efter avslutade studier på HOP1 vid Försvarshögskolan. Syftet med att skriva den var att försöka ge ett bidrag till anskaffningsprocessen av nya autonoma system till den svenska marinen. Detta gjordes genom att använda de tydligast framträdande dimensionerande teorierna ur svensk militärstrategisk doktrin och skapa ett analysverktyg utifrån detta, tillsammans med andra enligt författaren viktiga svenska kulturella principer, nämligen efterlevnaden av krigets lagar och hänsyn till kostnad. Studien är konstruerad som en kvalitativ, utforskande fallstudie med den svenska marinens undervattensförmåga som studerat fall. Resultaten visar på att marinen kan ha mycket stor nytta av dessa system, men att det initialt är viktigt att få ut system tidigt som direkt stödjer befintliga plattformar i deras verksamhet.

This project explores about different aspects of level of automation from a theoretical point of view and reflecting upon them by validating the ideas through benchmarking some of the SMEs in Småland region. This report includes a detailed description of SME’s and its importance, which is then narrowed down to the challenges they face due to technological advancements and changing market. The main purpose of this study is to analyze the level of automation, the SME’s in Småland region have adopted currently. This pre-study is carried out to gather information on a variety of products manufactured and the production system strategy in practice currently and carry out a pilot study to provide a systematic approach towards changing the level of automation. In order to differentiate the SME’s in Småland region according to their automation level, concepts like automation pyramid and level of automation taxonomies are used. LOA taxonomies are used to divide the various operations and tasks between humans and the automated systems. Whereas the automation pyramid is used to categorize the SME’s to their respective automation level. A contrast analysis is carried out to illustrate the automation levels in different companies based on the findings. Finally, the report is concluded considering the levels of automation the selected companies represent from the study and the overall automation level is proposed. Further recommendations are provided for industrial standardization with respect to automation.

The possibility of using multiple autonomous agents for completing various tasks has gathered a lot of interest in the last years. Using multiple agents does however come with the problems of choosing how to allocate the tasks and how the intelligence of the individual agents should be designed.  In this thesis a market inspired auction system for task allocation and an approach for generating a behavior tree creating the autonomous behavior using a library of available behaviors will be established. The validity of the proposed approach is demonstrated in simulation and with multiple experiments in a laboratory environment. The result does show that the approach promisingly solves the stated problems.

In industrial robotics, the detection of failures is a key part of the robotsprogram to reach a robust assembly process. However, the setting up of sucha detection is long, very specific to a robotic operation and involves programmingby engineers. In response to this problematic, the thesis presented inthis paper proposes an algorithm which makes it generic and semi-automaticthe creation of a failures detection and a classification method. Based onmachine learning, the algorithm is able to learn how to differentiate betweena success and a failure scenario given a series of examples. Moreover, theproposed method makes the teaching of failure detection/classification accessiblefor any operator without any special programming skills.After the programming of movements for a standard behavior, a trainingset of sensory acquisitions is recorded while the robot performs blindlyoperation cycles. Depending on sensors nature, the gathered signals canbe binary inputs, images, sounds or other information measured by specificsensors (force, enlightening, temperature...). These signals contain specificpatterns or signatures for success and failures. The set of training examples isthen analyzed by the clustering algorithm OPTICS. The algorithm providesan intuitive representation based on similarities between signals which helpsan operator to find the patterns to differenciate success and failure situations.The labels extracted from this analysis are thus taken into account to learna classification function. This last function is able to classify efficiently anew signal between the success and failure cases encountered in the trainingperiod and then to provide a relevant feedback to the robot program. Arecovery can then easily be defined by an operator to fix the flaw.